Activitywatch

ActivityWatch Computer Activity Statistics

Retrieve local ActivityWatch time-tracking data and analyze application usage, plus manage the ActivityWatch category configuration.

Quick Reference

cd skills/activitywatch/scripts

# Activity reports (default: daily)
uv run activitywatch.py a --daily
uv run activitywatch.py activity --weekly --top 5
uv run activitywatch.py a --monthly

# Pinned to a specific day / week / month
uv run activitywatch.py a --daily 2015-02-27
uv run activitywatch.py a --weekly 2026-07-08
uv run activitywatch.py a --monthly 2025-07

# Per-day sleep/routine view (first/last activity each day, + active time)
uv run activitywatch.py a --monthly --routine
uv run activitywatch.py a --monthly 2025-07 --routine
uv run activitywatch.py a --weekly --routine

# Category configuration
uv run activitywatch.py c get
uv run activitywatch.py c get -f out.json
uv run activitywatch.py c set -f new-config.json
uv run activitywatch.py c set -f new-config.json --no-backup

# Find uncategorized apps/titles (not matched by any rule)
uv run activitywatch.py c unclassified
uv run activitywatch.py c unclassified --weekly

CLI Commands

The script has two command groups, each with a short alias:

activity — Show activity reports

uv run activitywatch.py a [--daily | --weekly | --monthly] [DATE] [--routine] [--top N] [--url URL]

DATE is an optional positional argument that pins the report to a specific day or month instead of the current one. The format depends on the selected period (see below). It may also be passed as --date <value>.

Parameters:

At most one of --daily/--weekly/--monthly may be set. If none is given, --daily is assumed.

Pinning to a specific day / week / month

The optional DATE argument lets you query any past (or current) period. The format must match the selected period:

For the current day/week/month the end bound is now (the report is up-to-the-minute). For past periods the end bound is the natural end of that period (next day / next week / next month at startOfDay), so the full period is covered. The startOfDay and startOfWeek settings are always read from the ActivityWatch server to match the web UI.

# Activity on a specific day
uv run activitywatch.py a --daily 2015-02-27

# The week containing a specific date (week starts per server config)
uv run activitywatch.py a --weekly 2026-06-15

# A whole month
uv run activitywatch.py a --monthly 2025-07

# Equivalent flag form
uv run activitywatch.py a --monthly --date 2025-07

Invalid formats are rejected with a clear error, e.g. passing 2025-07-08 to --monthly or 2025-07 to --daily.

Output example:

Daily Activity Report
Period: 2026-07-08 10:25 ~ 16:56
Active: 4h28m
Top 10 applications:
  Tabbit Browser  48m47s (18.2%)
  WorkBuddy       40m12s (15.0%)
  firefox         37m35s (14.0%)
  OpenCode        33m14s (12.4%)
  ZenlessZoneZero 26m32s (9.9%)
  ...
Total: 4h28m across 30 apps (top 10 shown, covers 98.4%)

Category breakdown:
  🎬 娱乐           1h21m (30.2%)  - ZenlessZoneZero, MuMuNxDevice, Tabbit Browser
  💼 工作           1h16m (28.6%)  - WorkBuddy, OpenCode, firefox
  📚 学习           38m26s (14.3%)  - Tabbit Browser, firefox
  🛠️ 工具          28m45s (10.7%)  - Code, firefox, MiScreenShare
  🌐 浏览           21m48s (8.1%)  - firefox
  Uncategorized  19m41s (7.3%)  - qwenpaw-desktop, Tabbit Browser, clash-verge
  💬 通讯           1m42s (0.6%)  - olk

Top apps timeline  (gap<=3m  segs>=10m  top10)
  Tabbit Browser     11:57-12:16 18m30s  15:06-15:17 10m41s
  WorkBuddy          12:26-12:41 14m9s  14:01-14:17 15m48s
  firefox            13:54-14:17 22m43s
  OpenCode           10:44-11:03 19m15s  16:22-16:34 11m47s
  ZenlessZoneZero    11:19-11:44 24m47s
  MuMuNxDevice       10:29-10:44 14m42s  11:59-12:14 14m48s
(6 of 10 apps had no segments >= 10m)
Focus sessions (>=10m): 11  |  Longest: 24m47s (ZenlessZoneZero 11:19-11:44)

Apps are aggregated by data.app (one entry per application, not per window/tab). The display name is the app value as reported by aw-watcher-window, with a trailing .exe stripped (e.g. firefox.exe -> firefox). No window-title reverse lookup is performed — the app field on Windows is inconsistent (exe name for some apps, friendly name or title for others), so the report simply shows whichever form the watcher recorded.

Entries that resolve to the same name are merged.

• Title line: report title (Daily / Weekly / Monthly).
• Period line: Period: <first-activity> ~ <last-activity> - the actual first and last recorded activity timestamps in the period (local timezone), not the configured startOfDay bounds. This reflects when the user was really at the computer; idle/sleep/watcher-down gaps before the first and after the last event are not shown.
• Active line: Active: <duration> - the AFK-filtered active time only. No wall-clock fraction is shown, since the (often much wider) period span already implies the idle/sleep gap.
• Top applications: app name, duration, percentage (share of active time, sorted by duration descending). Each line is indented two spaces (no bullet/hyphen) and app names are padded to a common width so the durations and percentages align in a tidy column.
• Total line: total active duration and app count; when the app count exceeds --top, a (top N shown, covers X.X%) suffix is appended.
• Category breakdown: a top-level rollup of activity by category. Each event is classified with ActivityWatch's own semantics (every regex rule is tested against the app name or the window title; the deepest matching category path wins, with ties broken by later-in-list; events matching no rule land in Uncategorized). Category paths roll up to their first element. none-type ("No rule") categories match nothing - they only act as parent folders - so they never receive events directly. The section is omitted entirely when the server has no categories configured. The same app can appear under multiple categories because classification is per-event (app + title), so e.g. a browser's time is split by what its tabs were.
• Timeline: per-app long activity segments (see below).
• Focus line: Focus sessions (>=Nm): <count> | Longest: <dur> (<app> HH:MM-HH:MM) - a focus session is any merged same-app segment at least 10 minutes (600s) long, counted across all apps; the longest single segment is also reported.
• Durations use compact formatting: 45s, 37m, 4h12m, 1d3h.

Timeline

Below the category breakdown, a per-app timeline lists each top app's long activity segments. Adjacent events from the same app within 3 minutes are merged; only segments at least 10 minutes long are shown, in chronological order (left-to-right reads as the day's flow). Apps that have no long segments are omitted from the timeline, with a trailing (N of M apps had no segments >= Xm) line summarizing what was dropped. A line that exceeds 120 characters is truncated with +N more. Apps that switch too frequently (>= 6 long segments) collapse to one approximate `HH:MM-HH:MM (duration)range with a[switched frequently, approximate range only]` note.

When adjacent events are merged, the segment's reported duration spans the full start-to-end window, which includes the gap between the merged events (not just their combined active time).

The timeline is followed by a one-line focus summary (see the Focus line above).

Routine (--routine)

Add --routine to switch the report to a per-day sleep/routine view: instead of top apps and timelines, it prints one line per day with the first and last recorded activity time, plus the active duration - the AFK-filtered total time actually spent at the computer that day. Best with --weekly or --monthly; --daily is rejected (a single day has no per-day breakdown).

uv run activitywatch.py a --monthly --routine           # this month
uv run activitywatch.py a --monthly 2025-07 --routine   # a specific month
uv run activitywatch.py a --weekly --routine            # this week

Output example:

Monthly Routine
Period: 2026-07-01 ~ 2026-07-09
Day starts at: 04:00

  2026-07-01  08:50 ~ 03:57 (+1d)  active 9h12m
  2026-07-02  10:31 ~ 00:02 (+1d)  active 7h40m
  2026-07-03  09:00 ~ 01:07 (+1d)  active 8h05m
  2026-07-04  10:47 ~ 01:19 (+1d)  active 6h33m
  2026-07-05  11:01 ~ 02:43 (+1d)  active 10h18m
  2026-07-06  11:16 ~ 04:01 (+1d)  active 5h27m
  2026-07-07  04:01 ~ 01:28 (+1d)  active 11h02m
  2026-07-08  10:25 ~ 00:44 (+1d)  active 7h51m
  2026-07-09  10:48 ~ 14:05  (in progress)  active 3h17m

Each day shows the earliest and latest AFK-filtered activity time, plus the active duration - the AFK-filtered total time actually spent at the computer that day (sum of event durations), which is usually far less than the wall-clock start~end window because idle, away, and sleep gaps are excluded. A day's "routine day" boundary is offset by the server's startOfDay (e.g. 04:00 means a 03:00 event counts toward the previous day), so late-night sessions stay attached to the day they belong to rather than spilling into the next. Annotations:

• (+1d) — the last activity ran past midnight into the next calendar day.
• (in progress) — today; the day is not over yet, so the end time is now.
• (no activity) — no events recorded that day (keeps the calendar contiguous).

category — Get, set, or inspect category configuration

ActivityWatch categories are stored as a server setting (classes) and consist of a list of category entries, each with a name path, a classification rule, and optional metadata (color, id). Besides reading and writing the config, you can also list apps/titles that no rule matches — useful for discovering what new categories to add.

category get

uv run activitywatch.py c get [-f | --file out.json] [--url URL]

Read the current category configuration.

Without -f, prints the category list as pretty JSON to stdout. With -f, writes the JSON to the file and prints a confirmation.

Output example (stdout):

[
  {
    "name": ["Work", "Programming"],
    "rule": {
      "type": "regex",
      "regex": "Code|VSCode|Cursor",
      "ignore_case": true
    },
    "id": 1,
    "data": { "color": "#A8D0F0" }
  },
  {
    "name": ["Media", "Video"],
    "rule": { "type": "regex", "regex": "YouTube|VLC", "ignore_case": true },
    "id": 2,
    "data": { "color": "#FFD0A8" }
  }
]

category set

uv run activitywatch.py c set -f | --file config.json [--no-backup] [--url URL]

Replace the category configuration from a JSON file. The previous configuration is backed up by default to cache/categories-backup-<timestamp>.json before the new one is written. Pass --no-backup to skip the backup.

The input file must be a JSON array of category objects. Each object requires:

Rule object:

Roundtrip workflow: run c get -f my-config.json, edit the file, then c set -f my-config.json to apply changes. The backup file allows rollback if needed.

category unclassified

uv run activitywatch.py c unclassified [--daily | --weekly | --monthly] [DATE] [--url URL]

List app names and window titles not matched by any regex category rule. This is the counterpart to get/set: it tells you what still needs a category so you can decide which rules to add.

DATE is an optional positional argument that pins the scan to a specific day (YYYY-MM-DD for --daily/--weekly) or month (YYYY-MM for --monthly), exactly like the activity command.

Parameters:

At most one of --daily/--weekly/--monthly may be set. If none is given, --daily is assumed.

How it works: every regex rule is compiled and matched (substring search, honoring ignore_case) against both the app name and the window title of each event — the same logic ActivityWatch uses for classification. none-type (catch-all) rules are skipped, because they match everything. Any event whose app and title fail to match any regex rule contributes its app name and title to the output. The output lists one line per uncategorized app, with up to 3 sample window titles appended after - (comma-separated, ... if truncated) — nothing else.

Output example:

Uncategorized apps and titles
Period: 2026-07-08 04:00 ~ 15:58
  ApplicationFrameHost  -  通知中心, 设置, Window Dialog, ...
  clash-verge  -  Clash Verge
  consent  -  用户账户控制(UAC)
  firefox  -  New Tab - Mozilla Firefox, GitHub: Let's build from here, ...
  qianwen  -  千问
  Super Productivity  -  Super Productivity

If every event is already matched by existing rules, the output ends with All activity matched by existing category rules.

Typical workflow with get / set:

1. c unclassified — see which apps/titles lack a category.
2. c get -f my-config.json — export the current config.
3. Edit the file to add new regex rules covering the uncategorized entries.
4. c set -f my-config.json — apply (previous config is backed up automatically).
5. c unclassified again — verify the new rules took effect.

Key Rules

Dependencies

Managed automatically by uv via inline script metadata (no manual install needed):

• Python ≥ 3.12
• aw-client — official ActivityWatch Python client
• cyclopts — CLI framework
• pydantic — data validation

Run with uv (recommended):

uv run activitywatch.py a --daily

Notes